#Quality control metrices
The figures are showing different quality metrices for the control samples.
| Sample_Tag | nCells |
|---|---|
| Control | 17095 |
The figures are showing different quality metrices for the control samples.
The figures are showing different quality metrices for the control samples.
## `geom_smooth()` using formula = 'y ~ x'
The figures are showing different quality metrices for the control samples.
## Warning: Transformation introduced infinite values in continuous x-axis
## Warning: Removed 24 rows containing non-finite values (`stat_density()`).
The figures are showing different quality metrices for the control samples.
The figures are showing different quality metrices for the copanlisib samples.
| Sample_Tag | nCells |
|---|---|
| Copanlisib | 3382 |
The figures are showing different quality metrices for the copanlisib samples.
The figures are showing different quality metrices for the copanlisib samples.
## `geom_smooth()` using formula = 'y ~ x'
The figures are showing different quality metrices for the copanlisib samples.
## Warning: Transformation introduced infinite values in continuous x-axis
## Warning: Removed 4 rows containing non-finite values (`stat_density()`).
The figures are showing different quality metrices for the copanlisib samples.
The figures are showing different quality metrices for the alpelisib samples.
| Sample_Tag | nCells |
|---|---|
| Alpelisib | 21531 |
The figures are showing different quality metrices for the alpelisib samples.
The figures are showing different quality metrices for the alpelisib samples.
## `geom_smooth()` using formula = 'y ~ x'
The figures are showing different quality metrices for the alpelisib samples.
## Warning: Transformation introduced infinite values in continuous x-axis
## Warning: Removed 12 rows containing non-finite values (`stat_density()`).
The figures are showing different quality metrices for the alpelisib samples.
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in the
## data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical variable
## into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in the
## data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical variable
## into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in the
## data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical variable
## into a factor?
#Investigation of unwanted variations - cell cycle phases and mitochondrial gene expression
## Warning: The following features are not present in the object: UBR7, RFC2,
## RAD51, MCM2, TIPIN, MCM6, UNG, POLD3, WDR76, CLSPN, CDC45, CDC6, MSH2, MCM5,
## POLA1, MCM4, RAD51AP1, GMNN, RPA2, CASP8AP2, HELLS, E2F8, GINS2, PCNA, NASP,
## BRIP1, DSCC1, DTL, CDCA7, CENPU, ATAD2, CHAF1B, USP1, SLBP, RRM1, FEN1, RRM2,
## EXO1, CCNE2, TYMS, BLM, PRIM1, UHRF1, not searching for symbol synonyms
## Warning: The following features are not present in the object: NCAPD2, ANLN,
## TACC3, HMMR, GTSE1, NDC80, AURKA, TPX2, BIRC5, G2E3, CBX5, RANGAP1, CTCF,
## CDCA3, TTK, SMC4, ECT2, CENPA, CDC20, NEK2, CENPF, TMPO, HJURP, CKS2, DLGAP5,
## PIMREG, TOP2A, PSRC1, CDCA8, CKAP2, NUSAP1, KIF23, KIF11, KIF20B, CENPE,
## GAS2L3, KIF2C, NUF2, ANP32E, LBR, MKI67, CCNB2, CDC25C, HMGB2, CKAP2L, BUB1,
## CDK1, CKS1B, UBE2C, CKAP5, AURKB, CDCA2, TUBB4B, JPT1, not searching for symbol
## synonyms
## Warning in AddModuleScore(object = object, features = features, name = name, :
## Could not find enough features in the object from the following feature lists:
## S.Score Attempting to match case...Could not find enough features in the object
## from the following feature lists: G2M.Score Attempting to match case...
## Centering and scaling data matrix
## PC_ 1
## Positive: Cd69, Gm26870, Olfm4, Gm9733, Retnlg, Ifnb1, Cxcr1, Cd209a, Ly6g, Il23a
## Prok2, Cd209f, Vsig4, Hba-ps3, Calm4, Usp17lb, Ifnl2, Siglech, Klri2, Hba-a2
## Cd5l, Bcl2l14, Cd209g, Alas2, Ear-ps5, Slc40a1, Cma2, Ngp, Chad, Tcrg-C4
## Negative: Rpl36a, Rps16-ps2, Rps24-ps3, Rps10-ps1, Gm19810, Rps19-ps6, Rps3a3, Rps10-ps3, Gm6025, Gm46197
## Gm2225, Gm10095, Gm41073, Gm29228, Gm6136, Gm6341, Gm46317, CT025573.1, Gm6109, Gm15896
## Rpl26-ps6, Gm5528, Gm44010, Gm7424, Gm10086, Gm14044, Gm8186, Gm19688, Hnrnpa1, Gm7565
## PC_ 2
## Positive: Sparc, Nedd4, Fstl1, Col1a2, Col1a1, Fkbp9, Col3a1, Col5a2, Crtap, Loxl1
## Serping1, Bmp1, Serpinh1, Calu, Col5a1, Dcn, Fbn1, Rnase4, Mmp2, Bgn
## Pcolce, Adamts2, Fkbp7, Rcn1, Ppic, Serpinf1, Gstm2, Ikbip, Aebp1, Rcn3
## Negative: Gm4468, Gm46197, Gm46317, Gm29228, Gm6341, Gm15896, Gm44010, Gm6025, CT025573.1, Gm41073
## Gm10095, Rps10-ps3, Gm12739, Rpl26-ps6, Gm2178, Gm5528, Gm9687, Gm14044, Gm13692, AC110534.4
## Gm14874, Gm45499, Rpl31-ps9, Gm12788, Rps19-ps6, Rpl12-ps2, Gm5928, Gm11416, AC159898.1, Gm7351
## PC_ 3
## Positive: Pltp, Ctss, Tgfbi, Cybb, Lyz2, Pfn1, Ms4a6c, Ctsc, Ms4a7, Aif1
## Msr1, Cd38, C1qb, Dab2, Stab1, F13a1, C1qc, Cd72, Saa3, Ass1
## Maf, C1qa, Sdc4, Ehd4, Prdx1, Ly86, Cd74, Fabp5, Apoe, Arg1
## Negative: Col14a1, Dpt, Clec3b, Efemp1, Gm45860, Ogn, Ly6c1, Tnxb, Ly6a, Rpl17-ps4
## Ackr3, Dpp4, Gm7285, Scara5, Adamts5, Lrrn4cl, Adgrd1, Nid1, Itih5, Rpl31-ps9
## C1s1, Lgi2, Dcn, Fndc1, Gm4468, Col3a1, Fstl1, Gm45791, Rpl12-ps2, Gm5928
## PC_ 4
## Positive: Col12a1, Wisp1, Ncam1, Spon1, Lrrc15, C1qtnf3, Cdh11, Mical2, Postn, Thbs2
## Hs6st2, Col8a1, Dkk3, Adam12, Ltbp2, Kif26b, Wnt5a, Tagln, Unc5b, Gpr176
## Col5a2, Cald1, Adamts12, Tnc, Rflnb, Tpm2, Pmepa1, Olfml3, Col6a3, Bace1
## Negative: Cd34, Ly6c1, Clec3b, Tnxb, Efemp1, Ly6a, Dpt, Scara5, Dpp4, Ackr3
## Col14a1, Plpp3, Ogn, Cadm3, Cd55, Lrrn4cl, Gas7, Tgfbr2, Adamts5, Igfbp6
## Slfn5, Heg1, Sema3c, Efhd1, Adgrd1, Gsn, Itih5, Klf4, Scara3, Uap1
## PC_ 5
## Positive: Esam, Adgrf5, Mmrn2, Flt1, Ptprb, Pdlim1, Tspan7, Plvap, Tie1, Cdh5
## Egfl7, Col4a1, Ramp2, Emcn, Col4a2, Gimap4, Aqp1, Spns2, C130074G19Rik, Col15a1
## Myct1, Sox18, Palmd, Ece1, Cyyr1, Clec14a, Crip2, Gimap6, Erg, Ablim1
## Negative: Ctss, Ms4a6c, Ms4a7, Aif1, Cybb, Pltp, Ctsc, Lyz2, Msr1, C1qc
## C1qb, Tgfbi, C1qa, Ly86, Dab2, Cd72, F13a1, Sdc4, Mmp14, Saa3
## Cd38, Lgals3bp, Maf, Rgs1, Cd74, Apoe, Gatm, Cx3cr1, Runx3, Stab1
## 18:12:36 UMAP embedding parameters a = 0.9922 b = 1.112
## 18:12:36 Read 31748 rows and found 40 numeric columns
## 18:12:36 Using Annoy for neighbor search, n_neighbors = 30
## 18:12:36 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 18:12:44 Writing NN index file to temp file /tmp/RtmpBVuK46/file1c6393a8c845b
## 18:12:44 Searching Annoy index using 1 thread, search_k = 3000
## 18:12:55 Annoy recall = 75.37%
## 18:12:56 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 18:12:59 Initializing from normalized Laplacian + noise (using irlba)
## 18:13:02 Commencing optimization for 200 epochs, with 1606488 positive edges
## 18:13:23 Optimization finished
#Integration
#Clustering analysis
#Celltype identification singleR
#Label comparison
#Transcript analysis
## Rows: 34609 Columns: 4
## ── Column specification ───────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Cell_Index, Labels
## dbl (2): UMIs_human, UMIs_mouse
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#Celltype identification demultiplexed
## 18:16:11 UMAP embedding parameters a = 0.9922 b = 1.112
## 18:16:11 Read 31748 rows and found 30 numeric columns
## 18:16:11 Using Annoy for neighbor search, n_neighbors = 30
## 18:16:11 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 18:16:14 Writing NN index file to temp file /tmp/RtmpBVuK46/file1c63916deed44
## 18:16:14 Searching Annoy index using 1 thread, search_k = 3000
## 18:16:24 Annoy recall = 100%
## 18:16:25 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 18:16:28 Initializing from normalized Laplacian + noise (using irlba)
## 18:16:32 Commencing optimization for 200 epochs, with 1387376 positive edges
## 18:16:50 Optimization finished
## Labels
## Xenograft mainly_human mainly_mouse unique_mouse
## HN10621 74 3327 1742
## HN10960 1 106 69
## HN11097_Control 0 576 339
## HN11097_treated 27 650 329
## HN15239A_Alpelisib 84 7730 5038
## HN15239A_Control 29 10127 1500
## Labels
## Xenograft mainly_human mainly_mouse unique_mouse
## HN10621 2.330855e-03 1.047940e-01 5.486960e-02
## HN10960 3.149805e-05 3.338793e-03 2.173365e-03
## HN11097_Control 0.000000e+00 1.814288e-02 1.067784e-02
## HN11097_treated 8.504473e-04 2.047373e-02 1.036286e-02
## HN15239A_Alpelisib 2.645836e-03 2.434799e-01 1.586872e-01
## HN15239A_Control 9.134434e-04 3.189807e-01 4.724707e-02